1 citations · 1 across the 8 of their papers we have counts for
9 papers
PICPIs: Prediction-Interval-Conditional Prediction Intervals
Xuelin Yang, Baihe Huang, Yilong Hou +2
A classical question in statistics is which observable quantities to condition on when drawing inferences about unobservable targets. For conformal prediction in nonparametric unce…
Response Time Enhances Alignment with Heterogeneous Preferences
Federico Echenique, Alireza Fallah, Baihe Huang +1
Aligning large language models (LLMs) to human preferences typically relies on aggregating pooled feedback into a single reward model. However, this standard approach assumes that…
PivotRL: High Accuracy Agentic Post-Training at Low Compute Cost
Junkeun Yi, Damon Mosk-Aoyama, Baihe Huang +9
Post-training for long-horizon agentic tasks has a tension between compute efficiency and generalization. While supervised fine-tuning (SFT) is compute efficient, it often suffers…
Towards Anytime-Valid Statistical Watermarking
Baihe Huang, Eric Xu, Kannan Ramchandran +2
The proliferation of Large Language Models (LLMs) necessitates efficient mechanisms to distinguish machine-generated content from human text. While statistical watermarking has eme…
Sample Complexity and Representation Ability of Test-time Scaling Paradigms
Baihe Huang, Shanda Li, Tianhao Wu +5
Test-time scaling paradigms have significantly advanced the capabilities of large language models (LLMs) on complex tasks. Despite their empirical success, theoretical understandin…
Towards a Theoretical Understanding of the 'Reversal Curse' via Training Dynamics
Hanlin Zhu, Baihe Huang, Shaolun Zhang +4
Auto-regressive large language models (LLMs) show impressive capacities to solve many complex reasoning tasks while struggling with some simple logical reasoning tasks such as inve…
DAVED: Data Acquisition via Experimental Design for Data Markets
Charles Lu, Baihe Huang, Sai Praneeth Karimireddy +3
The acquisition of training data is crucial for machine learning applications. Data markets can increase the supply of data, particularly in data-scarce domains such as healthcare,…
Towards Optimal Statistical Watermarking
Baihe Huang, Hanlin Zhu, Banghua Zhu +4
We study statistical watermarking by formulating it as a hypothesis testing problem, a general framework which subsumes all previous statistical watermarking methods. Key to our fo…
On Representation Complexity of Model-based and Model-free Reinforcement Learning
Hanlin Zhu, Baihe Huang, Stuart Russell
We study the representation complexity of model-based and model-free reinforcement learning (RL) in the context of circuit complexity. We prove theoretically that there exists a br…